Text quality of printed documents is one of the most important factors in the evaluation of home or office printers. Consumers typically have a high expectation with respect to the quality of printed text. In general, consumers expect the text of printed documents to have smooth and sharp edges. In addition to the text quality, consumers expect the documents to be printed at a high rate of speed.
In contemporary digital printing systems, text are often rendered at one resolution but must be printed at a higher resolution. This process is commonly referred to as “text scaling”. One simple approach to text scaling is to replicate each pixel of an input document image to print the document image at a higher resolution. For example, if the input document image is to be printed at twice the original resolution, each pixel of the image is replicated to produce four pixels in a 2×2 pixel configuration. Consequently, the text is printed at twice the resolution of the original image. A concern with the pixel replication approach is that the scaled document image may have text edges that are not smooth as the original document image. Thus, the quality of the text may be significantly degraded as a result of the pixel replication.
There are a number of conventional text scaling techniques that can also enhance the text by smoothing the text edges of the scaled image. However, these conventional techniques are only designed for document images having black text on white background. Until recently, color printers were not affordable by the majority of consumers. Thus, the number of printers that can only print in black significantly outnumbered color printers. Consequently, improvements in the printing technology were primarily directed to document images having black text on white background. As a result, these conventional text scaling techniques are not suitable for enhancing document images having color text on color background.
However, there are general image scaling techniques that can scale color images, such as document images having color text on color background. Unlike the previously described text scaling techniques, these general image scaling techniques are designed for images with pictorial content, not for images with text. A large subset of the general image scaling techniques utilizes image interpolation, such as bilinear interpolation, cubic B-spline interpolation, and cubic convolution interpolation, to correct spatial distortion when images are scaled. Another subset of the general image scaling techniques utilizes edge detection to sharpen the detected edges of features contained in a given image.
A concern with the former subset of the general image scaling techniques is that these techniques are based on the assumption that the underlying image signal is continuous. That is, if two image intensities or colors are close in spatial proximity, then the intensities or the colors themselves are assumed to be close to each other. However, text images do not conform to this assumption, because the transitions between text and background are sharp, not continuous. Thus, these image interpolation techniques are not suitable for text images. In contrast, the latter subset of the general image scaling techniques are more suited for text images since they are based on preserving discontinuous edges. However, these edge-based scaling techniques are computationally intensive.
In addition to the above concerns, the general image scaling techniques produce output images that may contain colors that were not present in the input image. Thus, these techniques are not suited for implementation after halftoning and/or color matching stage of the printing process.
In view of these concerns, there is a need for a system and method for efficiently scaling and text enhancing document images having color text on color background using only the colors of the original document images.